Machine learning allows for an automated processing system (a “machine”), such as a computer system or specialized processing circuit, to develop generalizations about particular data sets and use the generalizations to solve associated problems by, for example, classifying new data. Once a machine learns generalizations from (or is trained using) known properties from the input or training data, it can apply the generalizations to future data to predict unknown properties.
A particular class of learning algorithms uses a technique that is sometimes called supervised learning. Supervised learning algorithms can be designed to map inputs to desired outputs or labels. Some machine learning algorithms use what is sometimes referred to as the kernel trick. The kernel trick allows for the mapping of observations from a general set S into a new space, without having to compute the mapping explicitly. In this manner the observations will gain (linear) structure in the new space. The observations can be based upon the similarity, distance or affinity between different entities
As but one example, Support Vector Machines (SVMs) represent a type of supervised learning algorithms that can use kernels. SVMs are based on the concept of decision planes that define decision boundaries. A decision plane is a hyperplane that separates between a set of objects having different class memberships. The hyperplane can be selected by increasing (or maximizing) margins for an SVM trained with samples from two classes. Samples on the margin are sometimes referred to as the support vectors. Other supervised learning algorithms can also use kernels.